Overview

Dataset statistics

Number of variables12
Number of observations15706
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory96.0 B

Variable types

Categorical1
Numeric11

Warnings

Degree is highly correlated with betweenesscentrality and 2 other fieldsHigh correlation
Eccentricity is highly correlated with closnesscentrality and 1 other fieldsHigh correlation
closnesscentrality is highly correlated with Eccentricity and 1 other fieldsHigh correlation
harmonicclosnesscentrality is highly correlated with Eccentricity and 1 other fieldsHigh correlation
betweenesscentrality is highly correlated with Degree and 1 other fieldsHigh correlation
triangles is highly correlated with Degree and 1 other fieldsHigh correlation
eigencentrality is highly correlated with Degree and 2 other fieldsHigh correlation
Degree is highly correlated with betweenesscentrality and 2 other fieldsHigh correlation
Eccentricity is highly correlated with closnesscentrality and 1 other fieldsHigh correlation
closnesscentrality is highly correlated with Eccentricity and 1 other fieldsHigh correlation
harmonicclosnesscentrality is highly correlated with Eccentricity and 2 other fieldsHigh correlation
betweenesscentrality is highly correlated with Degree and 1 other fieldsHigh correlation
triangles is highly correlated with Degree and 1 other fieldsHigh correlation
eigencentrality is highly correlated with Degree and 3 other fieldsHigh correlation
Degree is highly correlated with betweenesscentrality and 2 other fieldsHigh correlation
Eccentricity is highly correlated with closnesscentrality and 1 other fieldsHigh correlation
closnesscentrality is highly correlated with Eccentricity and 1 other fieldsHigh correlation
harmonicclosnesscentrality is highly correlated with Eccentricity and 1 other fieldsHigh correlation
betweenesscentrality is highly correlated with DegreeHigh correlation
triangles is highly correlated with DegreeHigh correlation
eigencentrality is highly correlated with DegreeHigh correlation
Eccentricity is highly correlated with betweenesscentrality and 2 other fieldsHigh correlation
betweenesscentrality is highly correlated with Eccentricity and 3 other fieldsHigh correlation
Degree is highly correlated with betweenesscentrality and 2 other fieldsHigh correlation
closnesscentrality is highly correlated with Eccentricity and 1 other fieldsHigh correlation
triangles is highly correlated with betweenesscentrality and 2 other fieldsHigh correlation
eigencentrality is highly correlated with betweenesscentrality and 2 other fieldsHigh correlation
harmonicclosnesscentrality is highly correlated with Eccentricity and 1 other fieldsHigh correlation
betweenesscentrality is highly skewed (γ1 = 26.70795507) Skewed
category has 565 (3.6%) zeros Zeros
avg_rating has 2263 (14.4%) zeros Zeros
betweenesscentrality has 4719 (30.0%) zeros Zeros
modularity_class has 246 (1.6%) zeros Zeros
triangles has 8841 (56.3%) zeros Zeros

Reproduction

Analysis started2021-12-03 19:29:18.147057
Analysis finished2021-12-03 19:29:35.238245
Duration17.09 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

group
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size122.8 KiB
Book
14308 
DVD
 
884
Video
 
461
Music
 
53

Length

Max length5
Median length4
Mean length3.976442124
Min length3

Characters and Unicode

Total characters62454
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBook
2nd rowBook
3rd rowBook
4th rowBook
5th rowBook

Common Values

ValueCountFrequency (%)
Book14308
91.1%
DVD884
 
5.6%
Video461
 
2.9%
Music53
 
0.3%

Length

2021-12-03T14:29:35.426522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-03T14:29:35.507309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
book14308
91.1%
dvd884
 
5.6%
video461
 
2.9%
music53
 
0.3%

Most occurring characters

ValueCountFrequency (%)
o29077
46.6%
B14308
22.9%
k14308
22.9%
D1768
 
2.8%
V1345
 
2.2%
i514
 
0.8%
d461
 
0.7%
e461
 
0.7%
M53
 
0.1%
u53
 
0.1%
Other values (2)106
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter44980
72.0%
Uppercase Letter17474
 
28.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o29077
64.6%
k14308
31.8%
i514
 
1.1%
d461
 
1.0%
e461
 
1.0%
u53
 
0.1%
s53
 
0.1%
c53
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
B14308
81.9%
D1768
 
10.1%
V1345
 
7.7%
M53
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin62454
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o29077
46.6%
B14308
22.9%
k14308
22.9%
D1768
 
2.8%
V1345
 
2.2%
i514
 
0.8%
d461
 
0.7%
e461
 
0.7%
M53
 
0.1%
u53
 
0.1%
Other values (2)106
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII62454
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o29077
46.6%
B14308
22.9%
k14308
22.9%
D1768
 
2.8%
V1345
 
2.2%
i514
 
0.8%
d461
 
0.7%
e461
 
0.7%
M53
 
0.1%
u53
 
0.1%
Other values (2)106
 
0.2%

sales_rank
Real number (ℝ≥0)

Distinct15393
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean280895.0623
Minimum16
Maximum2776380
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size122.8 KiB
2021-12-03T14:29:35.605044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile3474
Q137447
median173321
Q3429037.25
95-th percentile886637.75
Maximum2776380
Range2776364
Interquartile range (IQR)391590.25

Descriptive statistics

Standard deviation312256.5072
Coefficient of variation (CV)1.11164826
Kurtosis4.644929623
Mean280895.0623
Median Absolute Deviation (MAD)154569.5
Skewness1.800714888
Sum4411737849
Variance9.750412632 × 1010
MonotonicityNot monotonic
2021-12-03T14:29:35.706684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2153
 
< 0.1%
5090043
 
< 0.1%
38713
 
< 0.1%
1263
 
< 0.1%
106033
 
< 0.1%
1433
 
< 0.1%
53273
 
< 0.1%
57973
 
< 0.1%
252583
 
< 0.1%
3333
 
< 0.1%
Other values (15383)15676
99.8%
ValueCountFrequency (%)
161
< 0.1%
211
< 0.1%
231
< 0.1%
261
< 0.1%
291
< 0.1%
321
< 0.1%
371
< 0.1%
531
< 0.1%
601
< 0.1%
611
< 0.1%
ValueCountFrequency (%)
27763801
< 0.1%
27670021
< 0.1%
27067861
< 0.1%
25380011
< 0.1%
23784641
< 0.1%
23545571
< 0.1%
23397541
< 0.1%
22618471
< 0.1%
22577521
< 0.1%
22563321
< 0.1%

category
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.965172546
Minimum0
Maximum9
Zeros565
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size122.8 KiB
2021-12-03T14:29:35.807907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.17724549
Coefficient of variation (CV)0.5490922437
Kurtosis-0.4750508605
Mean3.965172546
Median Absolute Deviation (MAD)2
Skewness0.3785885744
Sum62277
Variance4.740397923
MonotonicityNot monotonic
2021-12-03T14:29:35.886588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
32754
17.5%
42734
17.4%
22589
16.5%
51978
12.6%
61654
10.5%
11273
8.1%
7922
 
5.9%
8773
 
4.9%
0565
 
3.6%
9464
 
3.0%
ValueCountFrequency (%)
0565
 
3.6%
11273
8.1%
22589
16.5%
32754
17.5%
42734
17.4%
51978
12.6%
61654
10.5%
7922
 
5.9%
8773
 
4.9%
9464
 
3.0%
ValueCountFrequency (%)
9464
 
3.0%
8773
 
4.9%
7922
 
5.9%
61654
10.5%
51978
12.6%
42734
17.4%
32754
17.5%
22589
16.5%
11273
8.1%
0565
 
3.6%

avg_rating
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.714758691
Minimum0
Maximum5
Zeros2263
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size122.8 KiB
2021-12-03T14:29:35.967720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.5
median4.5
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.632859002
Coefficient of variation (CV)0.4395599118
Kurtosis1.003913538
Mean3.714758691
Median Absolute Deviation (MAD)0.5
Skewness-1.566396262
Sum58344
Variance2.666228522
MonotonicityNot monotonic
2021-12-03T14:29:36.054676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4.54387
27.9%
53983
25.4%
43156
20.1%
02263
14.4%
3.51124
 
7.2%
3504
 
3.2%
2.5164
 
1.0%
276
 
0.5%
136
 
0.2%
1.513
 
0.1%
ValueCountFrequency (%)
02263
14.4%
136
 
0.2%
1.513
 
0.1%
276
 
0.5%
2.5164
 
1.0%
3504
 
3.2%
3.51124
 
7.2%
43156
20.1%
4.54387
27.9%
53983
25.4%
ValueCountFrequency (%)
53983
25.4%
4.54387
27.9%
43156
20.1%
3.51124
 
7.2%
3504
 
3.2%
2.5164
 
1.0%
276
 
0.5%
1.513
 
0.1%
136
 
0.2%
02263
14.4%

Degree
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct116
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.511524258
Minimum1
Maximum549
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size122.8 KiB
2021-12-03T14:29:36.159582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q35
95-th percentile8
Maximum549
Range548
Interquartile range (IQR)4

Descriptive statistics

Standard deviation11.87046293
Coefficient of variation (CV)2.631142436
Kurtosis434.1583941
Mean4.511524258
Median Absolute Deviation (MAD)2
Skewness16.4199566
Sum70858
Variance140.9078901
MonotonicityNot monotonic
2021-12-03T14:29:36.266329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14219
26.9%
53435
21.9%
42383
15.2%
22133
13.6%
31988
12.7%
6374
 
2.4%
7230
 
1.5%
8163
 
1.0%
9113
 
0.7%
1098
 
0.6%
Other values (106)570
 
3.6%
ValueCountFrequency (%)
14219
26.9%
22133
13.6%
31988
12.7%
42383
15.2%
53435
21.9%
6374
 
2.4%
7230
 
1.5%
8163
 
1.0%
9113
 
0.7%
1098
 
0.6%
ValueCountFrequency (%)
5491
< 0.1%
3241
< 0.1%
2541
< 0.1%
2251
< 0.1%
2231
< 0.1%
2191
< 0.1%
2131
< 0.1%
2071
< 0.1%
2011
< 0.1%
1962
< 0.1%

Eccentricity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.32006876
Minimum2
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size122.8 KiB
2021-12-03T14:29:36.357907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile12
Q113
median14
Q316
95-th percentile17
Maximum18
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.038096482
Coefficient of variation (CV)0.1423244899
Kurtosis6.455514962
Mean14.32006876
Median Absolute Deviation (MAD)1
Skewness-1.422818598
Sum224911
Variance4.15383727
MonotonicityNot monotonic
2021-12-03T14:29:36.443377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
143060
19.5%
152993
19.1%
132804
17.9%
162434
15.5%
121653
10.5%
171653
10.5%
18422
 
2.7%
11376
 
2.4%
10161
 
1.0%
3102
 
0.6%
Other values (3)48
 
0.3%
ValueCountFrequency (%)
21
 
< 0.1%
3102
 
0.6%
446
 
0.3%
91
 
< 0.1%
10161
 
1.0%
11376
 
2.4%
121653
10.5%
132804
17.9%
143060
19.5%
152993
19.1%
ValueCountFrequency (%)
18422
 
2.7%
171653
10.5%
162434
15.5%
152993
19.1%
143060
19.5%
132804
17.9%
121653
10.5%
11376
 
2.4%
10161
 
1.0%
91
 
< 0.1%

closnesscentrality
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5935
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1207422877
Minimum0.072685
Maximum0.762887
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size122.8 KiB
2021-12-03T14:29:36.539434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.072685
5-th percentile0.092818
Q10.107604
median0.117891
Q30.12996
95-th percentile0.1471995
Maximum0.762887
Range0.690202
Interquartile range (IQR)0.022356

Descriptive statistics

Standard deviation0.03263338363
Coefficient of variation (CV)0.2702730275
Kurtosis72.87452559
Mean0.1207422877
Median Absolute Deviation (MAD)0.0114465
Skewness7.308895916
Sum1896.37837
Variance0.001064937727
MonotonicityNot monotonic
2021-12-03T14:29:36.649400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.112923201
 
1.3%
0.11292584
 
0.5%
0.11292861
 
0.4%
0.11292759
 
0.4%
0.10566157
 
0.4%
0.11292452
 
0.3%
0.10565950
 
0.3%
0.12986749
 
0.3%
0.10146946
 
0.3%
0.07838342
 
0.3%
Other values (5925)15005
95.5%
ValueCountFrequency (%)
0.0726859
0.1%
0.07268619
0.1%
0.07268713
0.1%
0.07268816
0.1%
0.0726895
 
< 0.1%
0.072697
 
< 0.1%
0.0726922
 
< 0.1%
0.0726933
 
< 0.1%
0.0726951
 
< 0.1%
0.0726971
 
< 0.1%
ValueCountFrequency (%)
0.7628871
 
< 0.1%
0.5192981
 
< 0.1%
0.5138891
 
< 0.1%
0.4743592
 
< 0.1%
0.4728431
 
< 0.1%
0.4582041
 
< 0.1%
0.4484851
 
< 0.1%
0.447134
< 0.1%
0.4457833
 
< 0.1%
0.4444448
0.1%

harmonicclosnesscentrality
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7018
Distinct (%)44.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1324126644
Minimum0.077835
Maximum0.844595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size122.8 KiB
2021-12-03T14:29:36.759399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.077835
5-th percentile0.1014215
Q10.117598
median0.12914
Q30.142951
95-th percentile0.163358
Maximum0.844595
Range0.76676
Interquartile range (IQR)0.025353

Descriptive statistics

Standard deviation0.03514497208
Coefficient of variation (CV)0.2654200203
Kurtosis67.89098837
Mean0.1324126644
Median Absolute Deviation (MAD)0.012824
Skewness6.793061653
Sum2079.673307
Variance0.001235169062
MonotonicityNot monotonic
2021-12-03T14:29:36.869434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.131631201
 
1.3%
0.13167472
 
0.5%
0.13171753
 
0.3%
0.13166352
 
0.3%
0.1139750
 
0.3%
0.11395444
 
0.3%
0.14006842
 
0.3%
0.12331141
 
0.3%
0.13835540
 
0.3%
0.11273538
 
0.2%
Other values (7008)15073
96.0%
ValueCountFrequency (%)
0.0778357
< 0.1%
0.0778462
 
< 0.1%
0.0778513
 
< 0.1%
0.0778563
 
< 0.1%
0.0778621
 
< 0.1%
0.0778782
 
< 0.1%
0.0778881
 
< 0.1%
0.0778948
0.1%
0.0778992
 
< 0.1%
0.077916
< 0.1%
ValueCountFrequency (%)
0.8445951
< 0.1%
0.6069821
< 0.1%
0.6036041
< 0.1%
0.5337841
< 0.1%
0.5326581
< 0.1%
0.5292791
< 0.1%
0.4966221
< 0.1%
0.479731
< 0.1%
0.4786042
< 0.1%
0.4774771
< 0.1%

betweenesscentrality
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct5995
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58835.04419
Minimum0
Maximum30043724.2
Zeros4719
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size122.8 KiB
2021-12-03T14:29:36.988759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median721.671529
Q315555
95-th percentile93639.43184
Maximum30043724.2
Range30043724.2
Interquartile range (IQR)15555

Descriptive statistics

Standard deviation505180.6561
Coefficient of variation (CV)8.586390357
Kurtosis1113.272852
Mean58835.04419
Median Absolute Deviation (MAD)721.671529
Skewness26.70795507
Sum924063204
Variance2.552074953 × 1011
MonotonicityNot monotonic
2021-12-03T14:29:37.099795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04719
30.0%
15555427
 
2.7%
31109306
 
1.9%
0.2230
 
1.5%
0.25182
 
1.2%
0.333333174
 
1.1%
46662170
 
1.1%
7777118
 
0.8%
0.5102
 
0.6%
165
 
0.4%
Other values (5985)9213
58.7%
ValueCountFrequency (%)
04719
30.0%
0.0526323
 
< 0.1%
0.0555562
 
< 0.1%
0.11
 
< 0.1%
0.1111113
 
< 0.1%
0.1253
 
< 0.1%
0.1428578
 
0.1%
0.16666712
 
0.1%
0.1714291
 
< 0.1%
0.1859651
 
< 0.1%
ValueCountFrequency (%)
30043724.21
< 0.1%
18801887.481
< 0.1%
16353974.871
< 0.1%
12968098.111
< 0.1%
11980165.121
< 0.1%
10769213.471
< 0.1%
9890072.1061
< 0.1%
9862188.4711
< 0.1%
9648958.2791
< 0.1%
8898105.2981
< 0.1%

modularity_class
Real number (ℝ≥0)

ZEROS

Distinct39
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.86705718
Minimum0
Maximum38
Zeros246
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size122.8 KiB
2021-12-03T14:29:37.217104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median15
Q326
95-th percentile35
Maximum38
Range38
Interquartile range (IQR)19

Descriptive statistics

Standard deviation10.96208747
Coefficient of variation (CV)0.6499110874
Kurtosis-1.201104857
Mean16.86705718
Median Absolute Deviation (MAD)10
Skewness0.1807779518
Sum264914
Variance120.1673617
MonotonicityNot monotonic
2021-12-03T14:29:37.327138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
31123
 
7.2%
311019
 
6.5%
15789
 
5.0%
12751
 
4.8%
26727
 
4.6%
1724
 
4.6%
19701
 
4.5%
20700
 
4.5%
4698
 
4.4%
6672
 
4.3%
Other values (29)7802
49.7%
ValueCountFrequency (%)
0246
 
1.6%
1724
4.6%
2246
 
1.6%
31123
7.2%
4698
4.4%
5162
 
1.0%
6672
4.3%
7529
3.4%
8212
 
1.3%
9267
 
1.7%
ValueCountFrequency (%)
38161
 
1.0%
37167
 
1.1%
36240
 
1.5%
35313
 
2.0%
34280
 
1.8%
33170
 
1.1%
32298
 
1.9%
311019
6.5%
30191
 
1.2%
29500
3.2%

triangles
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct124
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.536992232
Minimum0
Maximum363
Zeros8841
Zeros (%)56.3%
Negative0
Negative (%)0.0%
Memory size122.8 KiB
2021-12-03T14:29:37.437102image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile7
Maximum363
Range363
Interquartile range (IQR)2

Descriptive statistics

Standard deviation11.56054342
Coefficient of variation (CV)4.556791019
Kurtosis265.354541
Mean2.536992232
Median Absolute Deviation (MAD)0
Skewness14.18492382
Sum39846
Variance133.6461641
MonotonicityNot monotonic
2021-12-03T14:29:37.548017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08841
56.3%
11989
 
12.7%
21525
 
9.7%
3977
 
6.2%
4673
 
4.3%
5457
 
2.9%
6301
 
1.9%
7170
 
1.1%
898
 
0.6%
973
 
0.5%
Other values (114)602
 
3.8%
ValueCountFrequency (%)
08841
56.3%
11989
 
12.7%
21525
 
9.7%
3977
 
6.2%
4673
 
4.3%
5457
 
2.9%
6301
 
1.9%
7170
 
1.1%
898
 
0.6%
973
 
0.5%
ValueCountFrequency (%)
3631
< 0.1%
2981
< 0.1%
2761
< 0.1%
2741
< 0.1%
2511
< 0.1%
2481
< 0.1%
2311
< 0.1%
2281
< 0.1%
2111
< 0.1%
2071
< 0.1%

eigencentrality
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7375
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01339435872
Minimum0.000672
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size122.8 KiB
2021-12-03T14:29:37.677990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.000672
5-th percentile0.000799
Q10.001793
median0.009573
Q30.017024
95-th percentile0.04110475
Maximum1
Range0.999328
Interquartile range (IQR)0.015231

Descriptive statistics

Standard deviation0.01974012599
Coefficient of variation (CV)1.473764173
Kurtosis477.18159
Mean0.01339435872
Median Absolute Deviation (MAD)0.0077355
Skewness13.75628461
Sum210.371798
Variance0.0003896725741
MonotonicityNot monotonic
2021-12-03T14:29:37.801633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.040639201
 
1.3%
0.00099948
 
0.3%
0.00080545
 
0.3%
0.00096143
 
0.3%
0.00076743
 
0.3%
0.00951942
 
0.3%
0.0059641
 
0.3%
0.00106241
 
0.3%
0.0017833
 
0.2%
0.04111733
 
0.2%
Other values (7365)15136
96.4%
ValueCountFrequency (%)
0.00067212
0.1%
0.00067612
0.1%
0.0006776
< 0.1%
0.0006788
0.1%
0.0006845
 
< 0.1%
0.000696
< 0.1%
0.0006923
 
< 0.1%
0.0006961
 
< 0.1%
0.000714
0.1%
0.0007033
 
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.5071151
< 0.1%
0.3676221
< 0.1%
0.3553661
< 0.1%
0.3527881
< 0.1%
0.345131
< 0.1%
0.3309841
< 0.1%
0.3069511
< 0.1%
0.2984351
< 0.1%
0.2982031
< 0.1%

Interactions

2021-12-03T14:29:20.466367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:20.593140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:20.707373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:20.821689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:20.925711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:21.057424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:21.185931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:21.325516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:21.452703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:21.575973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:21.699405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:21.888808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:21.991852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:22.095722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:22.202437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:22.307158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:22.415797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:22.524475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:22.642159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:22.758847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:22.860327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:22.973712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:23.084417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:23.200107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:23.307818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:23.415530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:23.516261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:23.626759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:23.726760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:23.846756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:23.961706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-12-03T14:29:24.185108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:24.287633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:24.395229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:24.492494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:24.599209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:24.698426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:24.811286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:24.927973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:25.049648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:25.262079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-12-03T14:29:25.490469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:25.605162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:25.718427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:25.842195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:25.960845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:26.068027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:26.202271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:26.322397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:26.437852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:26.570131image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:26.685523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:26.813956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:26.921098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:27.031068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:27.147339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:27.260664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:27.373861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:27.495624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:27.614307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-12-03T14:29:27.966288image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-12-03T14:29:30.899499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-12-03T14:29:31.139477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:31.250576image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:31.360576image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:31.481515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:31.601513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:31.721515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:31.841512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:31.963058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:32.082007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:32.192349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:32.306032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:32.422694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:32.536391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:32.651083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:32.776827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:32.888757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:33.013587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:33.133366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:33.236521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:33.356520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:33.477615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:33.591365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:33.696086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:33.802833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:33.908894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:34.025940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:34.140634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:34.254336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:34.364962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:34.584407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-03T14:29:34.694405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-12-03T14:29:37.902480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-03T14:29:38.235370image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-03T14:29:38.415370image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-03T14:29:38.616620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-03T14:29:34.879781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-03T14:29:35.117818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

groupsales_rankcategoryavg_ratingDegreeEccentricityclosnesscentralityharmonicclosnesscentralitybetweenesscentralitymodularity_classtriangleseigencentrality
0Book67741650.04150.1186110.1322356912.075529120.011472
1Book11417662.55140.1327130.144622192859.3141001100.008007
2Book1769334.015130.1330240.14798216412.90579031180.027384
3Book14434845.05150.1125180.124056872.1817543270.016311
4Book3183464.03160.1077230.12021215401.467030100.008706
5Video2333354.03140.1141120.1233971.1666671320.008597
6Book93367624.55130.1322190.14780580117.1224203100.016302
7Book72116260.02120.1418040.1573379672.0961352000.010110
8Video482874.02130.1129240.1316630.0000001210.042528
9Book13599044.51130.1284980.1388910.0000001500.008058

Last rows

groupsales_rankcategoryavg_ratingDegreeEccentricityclosnesscentralityharmonicclosnesscentralitybetweenesscentralitymodularity_classtriangleseigencentrality
15696DVD1600494.05170.0783820.08596831109.6250002120.009852
15697DVD6055573.52150.0927290.1033590.000000510.008214
15698DVD75104.55140.1018950.11471329801.394320500.006946
15699DVD3232362.53150.0927300.1034022.666667520.007793
15700DVD449575.09110.1326960.142182455.437637310.008894
15701DVD904945.01180.0727850.0781370.0000003700.000760
15702Book21551513.54160.1093700.1179187477.8161571700.007388
15703DVD401804.010150.0927350.1036481465.5918715170.017475
15704Book5831225.02170.0972910.10606020.326934100.001543
15705Book3481015.05150.0980670.1074151.7500003560.017470